Document clustering is an integral and important part of text mining. There are two types of clustering, namely, hard clustering and soft clustering. In case of hard clustering, data item belongs to only one cluster whereas in soft clustering, data point may fall into more than one cluster. Thus, soft clustering leads to fuzzy clustering wherein each data point is associated with a membership function that expresses the degree to which individual data points belong to the cluster. Accuracy is desired in information retrieval, which can be achieved by fuzzy clustering. In the work presented here, a fuzzy approach for text classification is used to classify the documents into appropriate clusters using Fuzzy C Means (FCM) clustering algorithm. Enron email dataset is used for experimental purpose. Using FCM clustering algorithm, emails are classified into different clusters. The results obtained are compared with the output produced by k means clustering algorithm. The comparative study showed that the fuzzy clusters are more appropriate than hard clusters.

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It is important for service-oriented architectures to consider about how the composition of web services affects business processes. For instance, a single web service may not have been adequate for most complex business operations, needing the use of multiple web services. This paper proposed a novel technique for optimal partitioning and execution of the services using a decentralized environment. The proposed technique is designed and developed using a genetic algorithm with multiple high task allocations on a single server. We compared three existing techniques, including meta-heuristic genetic algorithm, heuristics like Pooling-and-Greedy-Merge (PGM) technique, and Merge-by-Define-Use (MDU) technique, to a simulation of Business Process Execution Language (BPEL) partition using genetic algorithm through multiple high tasks allocation to single server node. The proposed technique is practical and advantageous. In terms of execution time, number of server requests, and throughput, the proposed technique outperformed the existing GA, PGM, and MDU techniques.

The significant consumption of video data over limited bandwidth mostly compromises in video quality. Video quality assessment (VQA) algorithms aim to estimate the quality of a distorted video data. For this, the scores are created in such a way that agrees with the quality judgments aligned to human visual perception. To measure the quality of the video, we need to compare the subjective score and objective score. Video quality assessment (VQA) is a challenging task due to the complexity of modelling perceived quality characteristics in both spatial and temporal domains. We proposed novel work on feature-based VQA. SIFT and SURF are used to compare the features in the original video against features of the distorted video. A mechanism using weighted features is illustrated to provide a better quality assessment. The subjective score is available in many image databases. Ex. LIVE, IPVL, and CSIQ database. An objective score is obtained by using formulas, and it is used to find a correlation between subjective score and objective score. The efficacy of the proposed method is evaluated against state-of-the-art VQA algorithms. Our method is observed to be consistent with the best VQA results.